Western Blot Quantifier

Automatically identify Western Blot gel bands, perform densitometric analysis, and calculate normalized values relative to loading controls (GAPDH, β-actin,...

Automatically identify Western Blot gel bands, perform densitometric analysis, and calculate normalized values relative to loading controls.

Features

• Automatic Band Detection: Detect protein band positions in gel images
• Densitometric Analysis: Calculate grayscale/optical density values for each band
• Normalization: Normalize relative to loading control proteins (e.g., GAPDH, β-actin, Tubulin)
• Data Export: Output quantitative results in CSV format

Usage

Basic Usage

# Call in Python
from skills.western_blot_quantifier.scripts.main import WesternBlotQuantifier

# Create analyzer
analyzer = WesternBlotQuantifier()

# Analyze single image
result = analyzer.analyze(
    image_path="path/to/wb_image.png",
    reference_bands=["GAPDH"],  # Loading control band names
    target_bands=["p53", "Bcl-2"],  # Target protein band names
    lane_positions=[0.2, 0.4, 0.6, 0.8]  # Lane positions (relative to image width)
)

print(result.summary())
result.save("output/quantification_results.csv")

Command Line Usage

python -m skills.western_blot_quantifier.scripts.main \
    --input path/to/wb_image.png \
    --reference GAPDH \
    --targets p53,Bcl-2 \
    --lanes 4 \
    --output results.csv

Parameter Description

Parameter	Description	Default
image_path	Gel image path	Required
reference_bands	Loading control protein name list	["GAPDH"]
target_bands	Target protein name list	[]
lane_positions	Lane position list	Auto-detect
threshold	Band detection threshold	0.1
background_correction	Background correction method	"rolling_ball"

Output Format

CSV Output Example

Lane,Protein,Raw_Intensity,Background,Corrected_Intensity,Normalized_to_Reference
1,GAPDH,125000.5,5000.2,120000.3,1.00
1,p53,85000.2,3000.1,82000.1,0.68
1,Bcl-2,62000.8,2500.5,59500.3,0.50
2,GAPDH,118000.3,4800.2,113200.1,1.00
...

Return Object

{
    "raw_data": DataFrame,           # Raw optical density data
    "normalized_data": DataFrame,    # Normalized data
    "band_regions": List[Dict],      # Detected band region coordinates
    "statistics": Dict,              # Statistical analysis results
    "figures": Dict                  # Visualization chart paths
}

Dependencies

numpy>=1.21.0
opencv-python>=4.5.0
pandas>=1.3.0
matplotlib>=3.4.0
scipy>=1.7.0
scikit-image>=0.18.0

Installation

pip install -r requirements.txt

Notes

1. Image Quality: High resolution, good contrast grayscale or black and white gel images are recommended
2. Loading Control Selection: Common loading controls include GAPDH, β-actin, Tubulin; selection depends on experimental conditions
3. Background Correction: Supports rolling_ball, median, none three background correction methods
4. Lane Marking: If auto-detection is inaccurate, lane positions can be manually specified

Examples

Example 1: Basic Analysis

from skills.western_blot_quantifier.scripts.main import WesternBlotQuantifier

analyzer = WesternBlotQuantifier()

# Analyze 4-lane Western Blot results
result = analyzer.analyze(
    image_path="experiment_data/wb_gel.png",
    reference_bands=["GAPDH"],
    target_bands=["p53", "p21"],
    lane_count=4
)

# View normalized results
print(result.normalized_data)

# Save charts
result.save_figures("output/")

Example 2: Batch Processing

import glob

analyzer = WesternBlotQuantifier()

for image_path in glob.glob("experiments/*.png"):
    result = analyzer.analyze(
        image_path=image_path,
        reference_bands=["β-actin"],
        target_bands=["Target_Protein"],
        lane_count=6
    )
    result.save(f"output/{Path(image_path).stem}_results.csv")

Algorithm Description

1. Image Preprocessing: Grayscale conversion → Background correction → Denoising
2. Lane Detection: Automatic lane boundary identification based on vertical projection analysis
3. Band Detection: Band localization using 1D Gaussian fitting or peak detection algorithms
4. Optical Density Calculation: Integrate grayscale values in band region, subtract background
5. Normalization: Target protein value / Loading control protein value

Author

OpenClaw Skills

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support